Computer vision, image processing and machine vision are phrases that describe approaches used to achieve the somewhat elusive goal of determining whether image data contains some specific object, feature or activity. One form of image processing relates to object recognition wherein classes of objects can be recognized. For example, Blippar is a visual discovery app that uses augmented reality and machine learning to identify objects through smartphones and wearables so that further information can be sought about the object. To use the Blippar app, the user can blipp (“scan”) objects they're curious about and the app will present content relative to the object, if the object is recognized by the app. Another example is Google Goggles that can be used for data searches based on pictures taken by a handheld device. For example, taking a picture of a famous landmark can be used to initiate a search for information about the landmark.
Detection within the image data is undertaken when the data is scanned for a specific condition. An example of a detection technique is the detection of a vehicle in an automatic road toll system. Detection is often based on relatively simple and fast computations and is sometimes used for finding smaller regions of interesting image data, which can then be further analyzed by more computationally demanding techniques.
Identification of an individual instance of an object, such as a specific person's face, fingerprint, identification of handwritten digits, or identification of a specific vehicle is another area of image processing that is undertaken in specific instances. For example, some facial recognition algorithms identify facial features by extracting features from an image of the person's face. The algorithm may analyze the relative position, size or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features. Other algorithms normalize a gallery of face images and then compress the facial data, only saving the data in the image that is useful for subsequent face recognition.
Three-dimensional facial recognition techniques use 3D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of the face, such as the contour of the eye sockets, nose, and chin. One advantage of 3D facial recognition is that it is not affected by changes in lighting like other techniques. It can also identify a face from a range of viewing angles, including a profile view.
Object recognition is dependent upon comparing relational data to similar data in a database. One problem with this technique is that it does not provide for spatial dimensioning from the image itself.
What is needed in the art is a method to identify machine attributes in an image taken by a mobile device.